About Me

Hi, thank you for visiting my online portfolio.

I am Enrico Laoh, PhD candidate in Industrial Engineering and Management, specializing in advancing human-AI collaborative systems for multi-stakes decision-making. My research integrates AI’s precision with human reasoning to address critical challenges. Additionally, I am exploring advanced technologies such as blockchain and continuous learning frameworks to create secure and adaptable AI systems for diverse applications. With multiple publications, awards, and leadership experiences, including guiding my INFORMS chapter to national recognition, I am dedicated to impactful research, interdisciplinary collaboration, and innovation. As a recipient of the i-CORPS Creativity, Innovation, and Entrepreneurship Scholar Award, funded by NASA, I have also developed a business plan to commercialize my research, emphasizing its real-world applicability and societal value.

Welcome to my homepage and please do not hestitate to contact me for more information.

My Expertise

• Interpretable & Incremental Machine Learning
  • Designing trustworthy AI frameworks that adapt to streaming, multi-group, and heterogeneous data.
  • Developing interpretable incremental learning models that reduce retraining costs, mitigate catastrophic forgetting, and preserve transparency.
  • Embedding fairness, temporal dynamics, and human-AI collaboration to ensure robustness and usability in different stakes decision-making.
• Applied Predictive Analytics & Decision Support
  • Translating data into actionable insights for healthcare, finance, operations, and infrastructure.
  • Integrating domain knowledge with data-driven methods to improve forecasting, anomaly detection, and risk management.
  • Building human-centered decision support systems that balance automation with interpretability and ethical responsibility.
• Unstructured & Multimodal Data Analytics
  • Analyzing text, images, and time-series signals to capture hidden patterns in real-world datasets.
  • Combining structured data with unstructured sources to enhance risk prediction and monitoring.
  • Leveraging cross-modal and bibliometric signals for feature selection and knowledge-guided modeling.
• Blockchain and Distributed Data Systems
  • Developing secure, permissioned blockchain frameworks for healthcare, finance, and supply chain.
  • Designing blockchain-EHR prototypes that integrate FHIR, IPFS, and smart contracts for privacy and interoperability.
  • Applying distributed ledgers for auditability and trust in sensitive decision-making systems.

Research Interests

My current research focuses on developing interpretable, continual, and fairness-aware machine learning frameworks to support multi-stakes decision-making systems. I am particularly interested in balancing adaptability, transparency, and human usability, with current emphasis on healthcare systems applications while expanding into finance, operations, and other business cases.

• Methodology
  Interpretable incremental learning, transfer learning, temporal modeling, federated learning, probabilistic and Bayesian methods, multivariate and time-series analysis, clustering and classification algorithms, bibliometric-guided feature selection, human-AI collaborative decision systems, and blockchain-integrated analytics.

• Applications
  Healthcare systems (data-driven disease prediction, chronic disease progression), financial risk modeling, supply chain analytics, infrastructure and power systems, military logistics, privacy-preserving health data platforms, customer analytics, and strategic management decision support.

Education

• Doctor of Philosophy, Industrial Engineering and Management, (all-but-dissertation) Oklahoma State University, Stillwater, United States
• Master of Science, Industrial Engineering and Management, (GPA 4.00/4.00) Oklahoma State University, Stillwater, United States
• Graduate Certificate, Business Analytics and Data Science, (GPA 4.00/4.00) Oklahoma State University, Stillwater, United States
• Master of Engineering, Data and Quality Engineering, (GPA 4.00/4.00) University of Indonesia, Depok, Indonesia
• Bachelor of Engineering, Industrial Engineering, (GPA 3.88/4.00) University of Indonesia, Depok, Indonesia

Special Thanks

I wish to extend my profound gratitude to the esteemed institutions that have generously provided funding for my research endeavors